Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
This work addresses texture segmentation for image analysis, but it is incremental as it builds on existing wavelet-based methods.
The paper tackled texture segmentation by evaluating wavelet choices and introducing empirical wavelets, showing that adaptive wavelets achieve better results than classic ones, with improvements demonstrated on six benchmarks.
Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. In order to focus only on the textural information, we also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.